Edge Operators

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Edge OperatorsEdge Operators

a kind of filtering that leads to a kind of filtering that leads to useful featuresuseful features

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Lines and ArcsLines and ArcsSegmentationSegmentation

In some image sets, lines, curves, and circular arcsare more useful than regions or helpful in additionto regions.

Lines and arcs are often used in

• object recognition

• stereo matching

• document analysis

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Edge DetectionEdge Detection

Basic idea: look for a neighborhood with strong signsof change.

81 82 26 2482 33 25 2581 82 26 24

Problems:

• neighborhood size

• how to detect change

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Differential OperatorsDifferential Operators

Differential operators

• attempt to approximate the gradient at a pixel via masks

• threshold the gradient to select the edge pixels

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Example: Sobel OperatorExample: Sobel Operator

-1 0 1 1 2 1Sx = -2 0 2 Sy = 0 0 0 -1 0 1 -1 -2 -1

On a pixel of the image I• let gx be the response to Sx• let gy be the response to Sy

And g = (gx + gy ) is the gradient magnitude.

= atan2(gy,gx) is the gradient direction.

2 2 1/2

Then the gradient is I = [gx gy] T

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Sobel Operator on the Blocks Sobel Operator on the Blocks ImageImage

original image gradient thresholded magnitude gradient magnitude

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Common Masks for Computing Common Masks for Computing GradientGradient

Sobel:Sobel:

Prewitt:Prewitt:

RobertsRoberts

-1 0 1-1 0 1-1 0 1

1 1 10 0 0-1 -1 -1

-1 0 1-2 0 2-1 0 1

1 2 10 0 0-1 -2 -1

0 1-1 0

1 00 -1

Sx Sy

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Zero Crossing OperatorsZero Crossing Operators

Motivation: The zero crossings of the second derivative of the image function are more precise than the peaks of the first derivative.

step edge

smoothed

1st derivative

2nd derivativezero crossing

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How do we estimate the Second How do we estimate the Second Derivative?Derivative?

Laplacian Filter: Laplacian Filter: f = f = f / f / x + x + f / f / yy

0 1 01 -4 10 1 0

2 2 2 2

• Standard mask implementation

• Derivation: In 1D, the first derivative can be computed with mask [-1 0 1]

• The 1D second derivative is [1 -2 1]*

• The Laplacian mask estimates the 2D second derivative.

2

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Properties of Derivative MasksProperties of Derivative Masks Coordinates of derivative masks have opposite signs in Coordinates of derivative masks have opposite signs in

order to obtain a high response in regions of high order to obtain a high response in regions of high contrast.contrast.

The sum of coordinates of derivative masks is zero, so The sum of coordinates of derivative masks is zero, so that a zero response is obtained on constant regions.that a zero response is obtained on constant regions.

First derivative masks produce high absolute values at First derivative masks produce high absolute values at points of high contrast.points of high contrast.

Second derivative masks produce zero-crossings at Second derivative masks produce zero-crossings at points of high contrast.points of high contrast.

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Marr/Hildreth OperatorMarr/Hildreth Operator

• First smooth the image via a Gaussian convolution.

• Apply a Laplacian filter (estimate 2nd derivative).

• Find zero crossings of the Laplacian of the Gaussian.

This can be done at multiple resolutions.

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Haralick OperatorHaralick Operator

• Fit the gray-tone intensity surface to a piecewise cubic polynomial approximation.

• Use the approximation to find zero crossings of the second directional derivative in the direction that maximizes the first directional derivative.

The derivatives here are calculated from direct mathematical expressions wrt the cubic polynomial.

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Canny Edge DetectorCanny Edge Detector

• Smooth the image with a Gaussian filter with spread .

• Compute gradient magnitude and direction at each pixel of the smoothed image.

• Zero out any pixel response the two neighboring pixels on either side of it, along the direction of the gradient.

• Track high-magnitude contours.

• Keep only pixels along these contours, so weak little segments go away.

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Canny Examples Canny Examples Canny =1 Canny =4

Canny =1 Roberts 2X2

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Canny on Kidney Canny on Kidney

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Canny CharacteristicsCanny Characteristics

The Canny operator gives single-pixel-wide The Canny operator gives single-pixel-wide images with good continuation between adjacent images with good continuation between adjacent pixelspixels

It is the most widely used edge operator today; It is the most widely used edge operator today; no one has done better since it came out in the no one has done better since it came out in the late 80s. Many implementations are available.late 80s. Many implementations are available.

It is very sensitive to its parameters, which need It is very sensitive to its parameters, which need to be adjusted for different application domains.to be adjusted for different application domains.